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Humanoid postural control based on two biologically-inspired approaches

机译:人形姿势控制基于两种生物启发方法

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This paper presents two approaches for controlling a humanoid upright posture. The first approach directly mimics the human postural control as understood and identified by biologist and neurologists while the second approach constitutes standard control engineering techniques arranged in a control architecture that is inspired by relevant biological findings. The former architecture is composed of an ankle angle feedback loop for body-foot control. Simple sensor fusions are used to yield estimates of external disturbances such as body support motion or contact forces having impact on the body. They are fed into the loop for disturbance compensation and upgrade it with the help of body-space signals from the equilibrium (vestibular) organs into a body-space control. A body-space command signal is used as set point signal for desired body position or motion and the error signal is fed into a linear PID controller. The latter architecture is composed of an extended observer that can estimate unknown external stimuli based on a dynamic model, a compensation mechanism, and a linear PID controller with the possibility of introducing PD feed-forward terms. Experiments on a special-purpose humanoid robot show the applicability of the two presented approaches in terms of stabilizing the system and tracking a desired voluntary motion in the presence of superimposed external stimuli. Although theoretically a large set of individual sensor combinations can be employed for estimation, using the vestibular sensors demonstrates a particularly robust estimation.
机译:本文呈现了两种控制人质直立姿势的方法。第一种方法直接模仿人体姿势控制,如生物学家和神经病学家所理解和鉴定,而第二种方法构成了以相关生物学发现的启发的控制架构中排列的标准控制工程技术。前者架构由脚踝角度反馈回路组成,用于体脚控制。简单的传感器融合用于产生外部干扰的估计,例如身体支撑运动或接触力对身体的影响。它们被送入循环以进行干扰补偿,并借助从平衡(前庭)器官的身体空间信号的帮助升级它到空间空间控制中。对于所需身体位置或运动,将车身空间指令信号用作设定点信号,并且误差信号被馈送到线性PID控制器中。后一种架构由扩展观察者组成,该扩展观察者可以基于动态模型,补偿机制和线性PID控制器来估计未知的外部刺激,其具有引入PD前馈术语的可能性。在特殊用途的人形机器人上的实验表明,在稳定系统并在叠加的外部刺激存在下跟踪所需的自愿运动,这两个呈现方法的适用性。尽管理论上可以采用大量的单独传感器组合来估计,但是使用前庭传感器演示了特别稳健的估计。

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